4.7 Article

Boosting quantum rotation gate embedded slime mould algorithm

期刊

EXPERT SYSTEMS WITH APPLICATIONS
卷 181, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2021.115082

关键词

Slime mould algorithm; Swarm intelligence; Quantum rotation gate; Water cycle; Engineering design

资金

  1. National Natural Science Foundation of China [62076185, U1809209]
  2. Zhejiang Provincial Natural Science Foundation of China [LY21F020001]
  3. Wenzhou Science AMP
  4. Technology Bureau [ZG2020026]
  5. Natural Science Foundation of Heilongjiang Province of China [LC2016024]
  6. Natural Science Foundation of the Jiangsu Higher Education Institutions [17KJB520044]
  7. Six Talent Peaks Project in Jiangsu Province [XYDXX108]
  8. Independent Innovation Fund Project of Tianjin University [2020XZC0101, 2020XYF0016, 2020XYF0103]

向作者/读者索取更多资源

The slime mould algorithm, proposed in 2020, simulates slime mould movement and behaviors to find optimal solutions but may suffer from slow convergence. An improved version, WQSMA, utilizes quantum rotation gates and water cycle mechanisms to balance exploration and exploitation, achieving better performance in solving practical engineering problems according to experimental results.
The slime mould algorithm is an interesting swarm-based algorithm proposed in 2020 based on this entity's trajectory finding abilities in nature. It simulates slime mould movement, foraging, and other behaviors to find the problem's optimal solution. Because of the complexity of the slime mould's trajectory, the SMA has strong randomness and makes the generated population diverse. However, in the late iteration of the algorithm, as the complexity of the problem to be dealt with increases, it tends to drop into the local best, and the convergence rate slows down. Therefore, in this study, an improved SMA, named WQSMA, is proposed to remedy the above imperfections. Specifically, the two strategies of quantum rotation gate and an operation from water cycle are used for the first time to improve the robustness of the original SMA. The purpose of adding both mechanisms is to keep the algorithm in equilibrium among exploration and exploitation inclinations. While expanding the search space of individual population, it also makes a more detailed exploration of the local area. The quantum rotation gate, which rotates by its small angle, can adequately exploit the algorithm and search in the local scope enough. Simultaneously, the water cycle mechanism can help the algorithm search thoroughly in the space to find the optimal solution. The improved algorithm was compared with 14 classical meta-heuristics and 14 advanced algorithms on the test set IEEE CEC 2014, and the results were obtained, with WQSMA ranking first in both comparisons. Also, to further illustrate the role of WQSMA in practical application, three engineering problems are used for verification. Experimental results show that WQSMA also performs well in solving such practical problems. A website at https://aliasgharheidari.com will support this research.

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